import numpy
import numpy as np
import torch
import matplotlib.pyplot as plt

from GradientDescent import cost_list

x_data = [1.0,2.0,3.0]
y_data = [2.0,4.0,6.0]

def forward(x) :
    return x * w  # 前馈过程

def loss(x,y):
    y_pred = forward(x)
    return (y_pred - y)*(y_pred - y) # 计算损失 使用MSE

w_list = []
mse_list = []

for w in np.arange(0.0,4.1,0.1):
    print('w=',w)
    l_sum = 0
    for x_val,y_val in zip(x_data,y_data):
        y_pred_val = forward(x_val)
        loss_val = loss(x_val,y_val)
        l_sum += loss_val
        print('\t',x_val,y_val,y_pred_val,loss_val)
    print('MSE=',l_sum/3)
    w_list.append(w)
    mse_list.append(l_sum/3)

# 线性模型的图像
# plt.plot(w_list,mse_list)
# plt.ylabel('Loss')
# plt.xlabel('w')
# plt.show()

# # GradientDes的图像
plt.plot(range(0,100),cost_list)
plt.xlabel('epoch')
plt.ylabel('cost')
plt.show()